计算机工程与应用Issue(11)：267-270,4.DOI:10.3778/j.issn.1002-8331.1206-0206
SOM和Elman神经网络在整流器故障诊断的应用
Application of SOM and ELMAN neural network on ;fault diagnosis of bridge rectifier
康洪铭 1李光升 2谢永成 3魏宁3
作者信息
- 1. 中国空气动力研究与发展中心低速所，四川 绵阳 621000
- 2. 装甲兵工程学院 控制工程系，北京 100072
- 3. 装甲兵工程学院 控制工程系，北京 100072
- 折叠
摘要
Abstract
A new diagnostic method based on SOM and Elman neural network for open or short faults of inner diodes in rectifier of armored vehicle power system is proposed. Through establishing the rectifier mode, FFT is used to get each fault mode’s harmonious number and magnitude, and the modes are classed by SOM network. Considering the phase dif-ference between material faults of some mode, the voltage value is sampled in period, and the faults are identified by Elman network. In results, this method achieves the goal of mode class and fault identify, moreover, it is feasible and correct.关键词
整流器/自组织映射(SOM)/Elman/故障诊断Key words
rectifier/Self-Organizing Map(SOM)/Elman/fault diagnosis分类
信息技术与安全科学引用本文复制引用
康洪铭,李光升,谢永成,魏宁..SOM和Elman神经网络在整流器故障诊断的应用[J].计算机工程与应用,2014,(11):267-270,4.
